The impact of sample volume in random search on the bbob test suite

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Abstract

Uniform Random Search is considered the simplest of all randomized search strategies and thus a natural baseline in benchmarking. Yet, in continuous domain it has its search domain width as a parameter that potentially has a strong effect on its performance. In this paper, we investigate this effect on the well-known 24 functions from the bbob test suite by varying the sample domain of the algorithm ([-a, a]n for a ? {0.5, 1, 2, 3, 4, 5, 6, 10, 20} and n the search space dimension). Though the optima of the bbob testbed are randomly chosen in [-4, 4]n (with the exception of the linear function f5), the best strategy depends on the search space dimension and the chosen budget. Small budgets and larger dimensions favor smaller domain widths.

Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1912-1919
Number of pages8
ISBN (Electronic)9781450367486
DOIs
Publication statusPublished - 13 Jul 2019
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019

Publication series

NameGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Country/TerritoryCzech Republic
CityPrague
Period13/07/1917/07/19

Keywords

  • Benchmarking
  • Black-box optimization

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